Legal claims defining the scope of protection, as filed with the USPTO.
2. The method of claim 1, wherein the generative network is trained to generate the output data with an output data schema matching a schema of a reference dataset.
3. The method of claim 2, wherein the model optimizer is configured to generate at least one of a statistical correlation score between the synthetic dataset and the reference dataset, a data similarity score between the synthetic dataset and the reference dataset, or a data quality score for the synthetic dataset.
4. The method of claim 3, wherein training the data model using the synthetic dataset comprises determining that the synthetic dataset satisfies a criterion concerning the at least one of the statistical correlation score between the synthetic dataset and the reference dataset, the data similarity score between the synthetic dataset and the reference dataset, or the data quality score for the synthetic dataset.
5. The method of claim 1, wherein the decoder input data in the code space has a second dimensionality less than the first dimensionality.
6. The method of claim 1, wherein the first representative point is a centroid of the one or more first points.
7. The method of claim 1, wherein the first representative point is a medoid of the one or more first points.
9. The system of claim 8, wherein the generative network is trained to generate the output data with an output data schema matching a schema of a reference dataset.
10. The system of claim 9, wherein the model optimizer is configured to generate at least one of a statistical correlation score between the synthetic dataset and the reference dataset, a data similarity score between the synthetic dataset and the reference dataset, and a data quality score for the synthetic dataset.
11. The system of claim 10, wherein training the data model using the synthetic dataset comprises determining that the synthetic dataset satisfies a criterion concerning the at least one of the statistical correlation score between the synthetic dataset and the reference dataset, the data similarity score between the synthetic dataset and the reference dataset, and the data quality score for the synthetic dataset.
12. The system of claim 8, wherein the decoder input data in the code space has a second dimensionality less than the first dimensionality.
13. The system of claim 8, wherein the first representative point is a centroid of the one or more first points.
14. The system of claim 8, wherein the first representative point is a medoid of the one or more first points.
16. The computer-readable medium of claim 15, wherein the generative network is trained to generate the output data with an output data schema matching a schema of a reference dataset.
17. The computer-readable medium of claim 16, wherein the model optimizer is configured to generate at least one of a statistical correlation score between the synthetic dataset and the reference dataset, a data similarity score between the synthetic dataset and the reference dataset, or a data quality score for the synthetic dataset.
18. The computer-readable medium of claim 17, wherein training the data model using the synthetic dataset comprises determining that the synthetic dataset satisfies a criterion concerning the at least one of the statistical correlation score between the synthetic dataset and the reference dataset, the data similarity score between the synthetic dataset and the reference dataset, or the data quality score for the synthetic dataset.
19. The computer-readable medium of claim 15, wherein the first representative point is a medoid or a centroid of the one or more first points.
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July 18, 2023
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